{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import tqdm\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "normal_img = os.listdir('normal')\n",
    "path = './normal'\n",
    "img_width = []\n",
    "img_height = []\n",
    "\n",
    "B = []\n",
    "G = []\n",
    "R = []\n",
    "\n",
    "for file in normal_img:\n",
    "    \n",
    "    file_path = os.path.join(path,file)\n",
    "    img = cv2.imread(file_path)\n",
    "    \n",
    "    B.append(np.mean(img[:,:,0]))\n",
    "    G.append(np.mean(img[:,:,1]))\n",
    "    R.append(np.mean(img[:,:,2]))\n",
    "    \n",
    "    img_width.append(img.shape[0])\n",
    "    img_height.append(img.shape[1])\n",
    "\n",
    "normal_img = os.listdir('restricted')\n",
    "path = './restricted'    \n",
    "for file in normal_img:\n",
    "    \n",
    "    file_path = os.path.join(path,file)\n",
    "    img = cv2.imread(file_path)\n",
    "    B.append(np.mean(img[:,:,0]))\n",
    "    G.append(np.mean(img[:,:,1]))\n",
    "    R.append(np.mean(img[:,:,2]))\n",
    "    \n",
    "    \n",
    "    img_width.append(img.shape[0])\n",
    "    img_height.append(img.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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85ORkL5oRWI7VtnG2rYt5ejYxIUWHBTM/LZZ9p5ro6HbZHY4KcN4kir1Ajohk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\npIlCKaXUkDRRKKWUGtL/B/rJt95x2AMTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1e8EI7z1d//ikbw0DWqIwnXprcxWl1Uf4wYxhthZ2EJs1NoPvnJfHMx/u5PX1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\nQB+jTe/l4REAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_width = np.array(img_width)\n",
    "img_height = np.array(img_height)\n",
    "sns.distplot(img_width)\n",
    "plt.show()\n",
    "sns.distplot(img_height)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VZdmMpsYv8lLjyEyKYVt1s9uhBB1LCMZVr+2pJyUuinPsrmTjJyLC2fmpbLeE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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TNJ0d4INXLmVhTprXJckctHZpHgAvnehQIESpM7fEnV8caGbfqS7eelEpq0py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\ncwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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H5gONPL3j+MBs5MsXF5OSpEV9Jb6cVRaZxLb9SAtXKimITL699e186efbeeVA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\nSgFFewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "B = np.array(B)\n",
    "G = np.array(G)\n",
    "R = np.array(R)\n",
    "\n",
    "sns.distplot(B)\n",
    "plt.show()\n",
    "sns.distplot(G)\n",
    "plt.show()\n",
    "sns.distplot(R)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "184.025962363007 211.61230265352327 208.74597601697286\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(B),np.mean(G),np.mean(R))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'id': 1, 'image_id': 0, 'category_id': 3, 'iscrowd': 0, 'segmentation': [], 'area': [], 'bbox': [88, 253, 118, 42], 'minAreaRect': [[88, 298], [86, 256], [203, 249], [206, 291]]}\n",
      "{'1': 317, '2': 1729, '3': 789, '4': 1783, '5': 697}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = open('train_no_poly.json',encoding='utf-8')\n",
    "gt = json.load(f)\n",
    "print(gt['annotations'][0])\n",
    "labels = {'1':0,'2':0,'3':0,'4':0,'5':0}\n",
    "areas = []\n",
    "for gt_info in gt['annotations']:  \n",
    "    #labels.append(gt_info['category_id'])\n",
    "    labels[str(gt_info['category_id'])] += 1\n",
    "    areas.append(gt_info['bbox'][2]*gt_info['bbox'][3])\n",
    "    \n",
    "print(labels)\n",
    "\n",
    "\n",
    "label_list = [labels[a] for a in labels]\n",
    "plt.bar(range(1,len(label_list)+1), label_list)  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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drLDuRpitoTrOsd4EGV1MKCJlJJc/ly8HOpxz+5xzQ8BGYP2obdYDD/vnTwDvMzPz5Rud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\nogAREZG8KEBERCQvChAREcnL/wcDhGqcKyGSsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180\n",
      "4125.417497648165\n",
      "43263\n"
     ]
    }
   ],
   "source": [
    " areas = np.array(areas)\n",
    "sns.distplot(areas)\n",
    "plt.show()\n",
    "print(np.min(areas))\n",
    "print(np.mean(areas))\n",
    "print(np.max(areas))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
